def apply_iit(db_session,
inst_combo_query,
lbcc_image,
use_indices,
los_image,
R0=1.01):
###### GET VARIABLE VALUES #####
meth_name = "IIT"
method_id_info = db_funcs.get_method_id(db_session,
meth_name,
meth_desc=None,
var_names=None,
var_descs=None,
create=False)
alpha_x_parameters = db_funcs.query_var_val(
db_session,
meth_name,
date_obs=lbcc_image.date_obs,
inst_combo_query=inst_combo_query)
alpha, x = alpha_x_parameters
##### APPLY IIT TRANSFORMATION ######
lbcc_data = lbcc_image.lbcc_data
corrected_iit_data = np.copy(lbcc_data)
corrected_iit_data[use_indices] = 10**(
alpha * np.log10(lbcc_data[use_indices]) + x)
# create IIT datatype
iit_image = psi_d_types.create_iit_image(los_image, lbcc_image,
corrected_iit_data,
method_id_info[0])
psi_d_types.LosImage.get_coordinates(iit_image, R0=R0)
return lbcc_image, iit_image, use_indices, alpha, x
def apply_iit_2(db_session, lbcc_image, use_indices, los_image, R0=1.01):
"""
Different from apply_iit() because it does not require pre-queried iit_combos.
This function finds the previous and next IIT coefs based on lbcc_image.date_obs
Parameters
----------
db_session
lbcc_image
use_indices
los_image
R0
Returns
-------
"""
###### GET VARIABLE VALUES #####
meth_name = "IIT"
method_id_info = db_funcs.get_method_id(db_session,
meth_name,
meth_desc=None,
var_names=None,
var_descs=None,
create=False)
theoretic_query = db_funcs.get_correction_pars(
db_session,
meth_name,
date_obs=lbcc_image.date_obs,
instrument=lbcc_image.instrument)
# separate alpha and x
alpha = theoretic_query[0]
x = theoretic_query[1]
##### APPLY IIT TRANSFORMATION ######
lbcc_data = lbcc_image.lbcc_data
corrected_iit_data = np.copy(lbcc_data)
corrected_iit_data[use_indices] = 10**(
alpha * np.log10(lbcc_data[use_indices]) + x)
# create IIT datatype
iit_image = psi_d_types.create_iit_image(los_image, lbcc_image,
corrected_iit_data,
method_id_info[0])
psi_d_types.LosImage.get_coordinates(iit_image, R0=R0)
return lbcc_image, iit_image, use_indices, alpha, x
ref_instrument = [
ref_inst,
]
euv_images = db_funcs.query_euv_images(db_session,
time_min=hist_query_time_min,
time_max=hist_query_time_max,
instrument=ref_instrument)
# get min and max carrington rotation
rot_max = euv_images.cr_rot.max()
rot_min = euv_images.cr_rot.min()
# method information
meth_name = "IIT"
method_id = db_funcs.get_method_id(db_session,
meth_name,
meth_desc=None,
var_names=None,
var_descs=None,
create=False)
# query for IIT histograms
pd_lbc_hist = db_funcs.query_hist(db_session=db_session,
meth_id=method_id[1],
n_intensity_bins=n_intensity_bins,
lat_band=lat_band,
time_min=hist_query_time_min,
time_max=hist_query_time_max)
pd_lbc_hist_srt = pd_lbc_hist.sort_values(by=['image_id'])
# convert the binary types back to arrays
mu_bin_edges, intensity_bin_edges, full_lbc_hist = psi_d_types.binary_to_hist(
pd_lbc_hist_srt, n_mu_bins=None, n_intensity_bins=n_intensity_bins)
# create corrected/original histograms
db_session = init_db_conn_old(db_name=use_db, chd_base=db_class.Base, sqlite_path=sqlite_path)
elif use_db in ['mysql-Q', 'mysql-Q_test']:
# setup database connection to MySQL database on Q
db_session = init_db_conn_old(db_name=use_db, chd_base=db_class.Base, user=user, password=password)
# start time
start_time_tot = time.time()
# creates mu bin & intensity bin arrays
mu_bin_edges = np.linspace(0.1, 1.0, n_mu_bins + 1, dtype='float')
image_intensity_bin_edges = np.linspace(0, 5, num=n_intensity_bins + 1, dtype='float')
# create LBC method
meth_name = 'LBCC'
meth_desc = 'LBCC Theoretic Fit Method'
method_id = get_method_id(db_session, meth_name, meth_desc, var_names=None, var_descs=None, create=True)
# loop over instrument
for instrument in inst_list:
# query EUV images
query_instrument = [instrument, ]
query_pd_all = query_euv_images(db_session=db_session, time_min=hist_query_time_min,
time_max=hist_query_time_max, instrument=query_instrument,
wavelength=wavelengths)
# query LBCC histograms
hist_pd = query_hist(db_session, meth_id=method_id[1], n_mu_bins=n_mu_bins, n_intensity_bins=n_intensity_bins,
lat_band=lat_band, time_min=hist_query_time_min, time_max=hist_query_time_max,
instrument=query_instrument, wavelength=wavelengths)
# compare image results to hist results based on image_id
in_index = query_pd_all.data_id.isin(hist_pd.image_id)
mu_bin_centers = (mu_bin_array[1:] + mu_bin_array[:-1]) / 2
# time arrays
# returns array of moving averages center dates, based off start and end date
moving_avg_centers, moving_width = lbcc.moving_averages(query_time_min,
query_time_max,
weekday,
days=number_of_days)
# retrieve beta and y for all instruments
# plot all three instruments beta plot, then y plot
linestyles = ['dashed']
marker_types = ['None']
meth_name = 'IIT'
method_id = db_funs.get_method_id(db_session, meth_name, create=False)
plot_alpha = np.zeros((moving_avg_centers.__len__(), inst_list.__len__()))
plot_x = np.zeros((moving_avg_centers.__len__(), inst_list.__len__()))
plot_mean = np.zeros((moving_avg_centers.__len__(), inst_list.__len__()))
plot_std = np.zeros((moving_avg_centers.__len__(), inst_list.__len__()))
plot_act_mean = np.zeros((moving_avg_centers.__len__(), inst_list.__len__()))
plot_mean_act = np.zeros((moving_avg_centers.__len__(), inst_list.__len__()))
for inst_index, instrument in enumerate(inst_list):
print("Interpolating IIT parameters for " + instrument + ".")
# get all instrument histograms
inst_hist_pd = db_funs.query_hist(
db_session=db_session,
meth_id=method_id[1],
def plot_iit_histograms(db_session,
hdf_data_dir,
hist_query_time_min,
hist_query_time_max,
inst_list,
ref_inst,
n_intensity_bins=200,
lat_band=[-np.pi / 2.4, np.pi / 2.4],
R0=1.01,
log10=True):
# start time
start_time = time.time()
#### GET REFERENCE INFO FOR LATER USE ####
# get index number of reference instrument
ref_index = inst_list.index(ref_inst)
# query euv images to get carrington rotation range
ref_instrument = [
ref_inst,
]
euv_images = db_funcs.query_euv_images(db_session,
time_min=hist_query_time_min,
time_max=hist_query_time_max,
instrument=ref_instrument)
# get min and max carrington rotation
rot_max = euv_images.cr_rot.max()
rot_min = euv_images.cr_rot.min()
# method information
meth_name = "IIT"
method_id = db_funcs.get_method_id(db_session,
meth_name,
meth_desc=None,
var_names=None,
var_descs=None,
create=False)
# query for IIT histograms
pd_lbc_hist = db_funcs.query_hist(db_session=db_session,
meth_id=method_id[1],
n_intensity_bins=n_intensity_bins,
lat_band=lat_band,
time_min=hist_query_time_min,
time_max=hist_query_time_max)
pd_lbc_hist_srt = pd_lbc_hist.sort_values(by=['image_id'])
# convert the binary types back to arrays
mu_bin_edges, intensity_bin_edges, full_lbc_hist = psi_d_types.binary_to_hist(
pd_lbc_hist_srt, n_mu_bins=None, n_intensity_bins=n_intensity_bins)
# create corrected/original histograms
original_hist_list = np.full(full_lbc_hist.shape, 0, dtype=np.int64)
corrected_hist_list = np.full(full_lbc_hist.shape, 0, dtype=np.int64)
for inst_index, instrument in enumerate(inst_list):
print("Applying corrections for", instrument)
#### QUERY IMAGES ####
query_instrument = [
instrument,
]
rot_images = db_funcs.query_euv_images_rot(db_session,
rot_min=rot_min,
rot_max=rot_max,
instrument=query_instrument)
image_pd = rot_images.sort_values(by=['cr_rot'])
# get time minimum and maximum for instrument
inst_time_min = rot_images.date_obs.min()
inst_time_max = rot_images.date_obs.max()
# query correct image combos
lbc_meth_name = "LBCC"
combo_query_lbc = db_funcs.query_inst_combo(db_session, inst_time_min,
inst_time_max,
lbc_meth_name, instrument)
iit_meth_name = "IIT"
combo_query_iit = db_funcs.query_inst_combo(db_session, inst_time_min,
inst_time_max,
iit_meth_name, instrument)
# query correct image combos
combo_query_lbc = db_funcs.query_inst_combo(db_session,
hist_query_time_min,
hist_query_time_max,
meth_name="LBCC",
instrument=instrument)
# query correct image combos
combo_query_iit = db_funcs.query_inst_combo(db_session,
hist_query_time_min,
hist_query_time_max,
meth_name="IIT",
instrument=instrument)
for index, row in image_pd.iterrows():
# apply LBC
original_los, lbcc_image, mu_indices, use_indices, theoretic_query = lbcc_funcs.apply_lbc(
db_session,
hdf_data_dir,
combo_query_lbc,
image_row=row,
n_intensity_bins=n_intensity_bins,
R0=R0)
#### ORIGINAL LOS DATA ####
# calculate IIT histogram from original data
original_los_hist = psi_d_types.LosImage.iit_hist(
original_los, intensity_bin_edges, lat_band, log10)
# add 1D histogram to array
original_hist_list[:, index] = original_los_hist
#### CORRECTED DATA ####
# apply IIT correction
lbcc_image, iit_image, use_indices, alpha, x = iit_funcs.apply_iit(
db_session,
combo_query_iit,
lbcc_image,
use_indices,
original_los,
R0=R0)
#### CREATE CORRECTED IIT HISTOGRAM #####
# calculate IIT histogram from LBC
hist_iit = psi_d_types.IITImage.iit_hist(iit_image, lat_band,
log10)
# create IIT histogram datatype
corrected_hist = psi_d_types.create_iit_hist(
iit_image, method_id[1], lat_band, hist_iit)
corrected_hist_list[:, index] = corrected_hist.hist
# plotting definitions
color_list = ['red', 'blue', 'black']
linestyle_list = ['solid', 'dashed', 'dashdot']
#### CREATE NEW HISTOGRAM ####
for inst_index, instrument in enumerate(inst_list):
print("Plotting Histograms for", instrument)
#### GET INDICES TO USE ####
# get index of instrument in histogram dataframe
hist_inst = pd_lbc_hist_srt['instrument']
pd_inst_index = hist_inst[hist_inst == instrument].index
#### ORIGINAL HISTOGRAM #####
# define histogram
original_hist = original_hist_list[:, pd_inst_index].sum(axis=1)
# normalize histogram
row_sums = original_hist.sum(axis=0, keepdims=True)
norm_original_hist = original_hist / row_sums
# plot original
Plotting.Plot1d_Hist(norm_original_hist,
instrument,
inst_index,
intensity_bin_edges,
color_list,
linestyle_list,
figure=100,
xlabel="Intensity (log10)",
ylabel="H(I)",
title="Histogram: Original LOS Data")
#### LBCC HISTOGRAM #####
# define histogram
lbc_hist = full_lbc_hist[:, pd_inst_index].sum(axis=1)
# normalize histogram
lbc_sums = lbc_hist.sum(axis=0, keepdims=True)
norm_lbc_hist = lbc_hist / lbc_sums
# plot lbcc
Plotting.Plot1d_Hist(norm_lbc_hist,
instrument,
inst_index,
intensity_bin_edges,
color_list,
linestyle_list,
figure=200,
xlabel="Intensity (log10)",
ylabel="H(I)",
title="Histogram: Post LBCC")
#### CORRECTED HISTOGRAM ####
# define histogram
corrected_hist = corrected_hist_list[:, pd_inst_index].sum(axis=1)
# normalize histogram
iit_sums = corrected_hist.sum(axis=0, keepdims=True)
norm_corrected_hist = corrected_hist / iit_sums
# plot corrected
Plotting.Plot1d_Hist(norm_corrected_hist,
instrument,
inst_index,
intensity_bin_edges,
color_list,
linestyle_list,
figure=300,
xlabel="Intensity (log10)",
ylabel="H(I)",
title="Histogram: Post IIT")
# end time
end_time = time.time()
print("ITT has been applied and original/resulting histograms plotted.")
print("Total elapsed time to apply correction and plot histograms: " +
str(round(end_time - start_time, 3)) + " seconds.")
return None
def create_histograms(db_session,
inst_list,
lbc_query_time_min,
lbc_query_time_max,
hdf_data_dir,
n_intensity_bins=200,
lat_band=[-np.pi / 2.4, np.pi / 2.4],
log10=True,
R0=1.01,
wavelengths=None):
"""
create and save (to database) IIT-Histograms from LBC Data
@param db_session: connected db session for querying EUV images and saving histograms
@param inst_list: list of instruments
@param lbc_query_time_min: minimum query time for applying lbc fit
@param lbc_query_time_max: maximum query time for applying lbc fit
@param hdf_data_dir: directory of processed images to plot original images
@param n_intensity_bins: number of intensity bins
@param lat_band: latitude band
@param log10: boolean value
@param R0: radius
@return: None, saves histograms to database
"""
# start time
start_time = time.time()
# create IIT method
meth_name = "IIT"
meth_desc = "IIT Fit Method"
method_id = db_funcs.get_method_id(db_session,
meth_name,
meth_desc,
var_names=None,
var_descs=None,
create=True)
for instrument in inst_list:
print("Beginning loop for instrument:", instrument)
# query EUV images
query_instrument = [
instrument,
]
image_pd_all = db_funcs.query_euv_images(db_session=db_session,
time_min=lbc_query_time_min,
time_max=lbc_query_time_max,
instrument=query_instrument,
wavelength=wavelengths)
# query LBCC histograms
hist_pd = db_funcs.query_hist(db_session,
meth_id=method_id[1],
n_intensity_bins=n_intensity_bins,
lat_band=lat_band,
time_min=lbc_query_time_min,
time_max=lbc_query_time_max,
instrument=query_instrument,
wavelength=wavelengths)
if hist_pd.shape[0] == 0:
# use all images in range
in_index = pd.Series([False] * image_pd_all.shape[0])
else:
# compare image results to hist results based on image_id
in_index = image_pd_all.image_id.isin(hist_pd.image_id)
# return only images that do not have corresponding histograms
image_pd = image_pd_all[~in_index]
# check that images remain that need histograms
if image_pd.shape[0] == 0:
print("All " + instrument +
" images in timeframe already have associated histograms.")
continue
# apply LBC
for index, row in image_pd.iterrows():
print("Calculating IIT histogram at time:", row.date_obs)
original_los, lbcc_image, mu_indices, use_indices, theoretic_query = lbcc_funcs.apply_lbc_2(
db_session,
hdf_data_dir,
image_row=row,
n_intensity_bins=n_intensity_bins,
R0=R0)
# check that image load and LBCC application finished successfully
if original_los is None:
continue
# calculate IIT histogram from LBC
hist = psi_d_types.LBCCImage.iit_hist(lbcc_image, lat_band, log10)
# create IIT histogram datatype
iit_hist = psi_d_types.create_iit_hist(lbcc_image, method_id[1],
lat_band, hist)
# add IIT histogram and meta data to database
db_funcs.add_hist(db_session, iit_hist)
end_time = time.time()
print(
"Inter-instrument transformation histograms have been created and saved to the database."
)
print("Total elapsed time for histogram creation: " +
str(round(end_time - start_time, 3)) + " seconds.")
return None
def calc_iit_coefficients(db_session,
inst_list,
ref_inst,
calc_query_time_min,
calc_query_time_max,
weekday=0,
number_of_days=180,
image_freq=2,
image_del=np.timedelta64(30, 'm'),
n_intensity_bins=200,
lat_band=[-np.pi / 2.4, np.pi / 2.4],
create=False,
wavelengths=None):
# start time
start_time = time.time()
# create IIT method
meth_name = "IIT"
meth_desc = "IIT Fit Method"
method_id = db_funcs.get_method_id(db_session,
meth_name,
meth_desc,
var_names=None,
var_descs=None,
create=False)
#### GET REFERENCE INFO FOR LATER USE ####
# get index number of reference instrument
ref_index = inst_list.index(ref_inst)
# query euv images to get carrington rotation range
ref_instrument = [
ref_inst,
]
euv_images = db_funcs.query_euv_images(db_session,
time_min=calc_query_time_min,
time_max=calc_query_time_max,
instrument=ref_instrument,
wavelength=wavelengths)
# get min and max carrington rotation
rot_max = euv_images.cr_rot.max()
rot_min = euv_images.cr_rot.min()
# calculate the moving average centers
ref_moving_avg_centers, moving_width = lbcc.moving_averages(
calc_query_time_min, calc_query_time_max, weekday, number_of_days)
# calculate image cadence centers
range_min_date = ref_moving_avg_centers[0] - moving_width / 2
range_max_date = ref_moving_avg_centers[-1] + moving_width / 2
image_centers = synch_utils.get_dates(
time_min=range_min_date.astype(datetime.datetime),
time_max=range_max_date.astype(datetime.datetime),
map_freq=image_freq)
# query histograms
ref_hist_pd = db_funcs.query_hist(
db_session=db_session,
meth_id=method_id[1],
n_intensity_bins=n_intensity_bins,
lat_band=lat_band,
time_min=calc_query_time_min - datetime.timedelta(days=number_of_days),
time_max=calc_query_time_max + datetime.timedelta(days=number_of_days),
instrument=ref_instrument,
wavelength=wavelengths)
# keep only one observation-histogram per image_center window
keep_ind = lbcc.cadence_choose(ref_hist_pd.date_obs, image_centers,
image_del)
ref_hist_pd = ref_hist_pd.iloc[keep_ind]
# convert binary to histogram data
mu_bin_edges, intensity_bin_edges, ref_full_hist = psi_d_types.binary_to_hist(
hist_binary=ref_hist_pd,
n_mu_bins=None,
n_intensity_bins=n_intensity_bins)
# determine date of first AIA image
min_ref_time = db_session.query(func.min(
db_class.EUV_Images.date_obs)).filter(
db_class.EUV_Images.instrument == ref_inst).all()
base_ref_min = min_ref_time[0][0]
base_ref_center = base_ref_min + datetime.timedelta(
days=number_of_days) / 2
base_ref_max = base_ref_center + datetime.timedelta(
days=number_of_days) / 2
if (calc_query_time_min - datetime.timedelta(days=7)) < base_ref_center:
# generate histogram for first year of reference instrument
ref_base_hist = ref_full_hist[:, (
ref_hist_pd['date_obs'] >= str(base_ref_min)) & (
ref_hist_pd['date_obs'] <= str(base_ref_max))]
else:
ref_base_hist = None
for inst_index, instrument in enumerate(inst_list):
# check if this is the reference instrument
if inst_index == ref_index:
# loop through moving average centers
for date_index, center_date in enumerate(ref_moving_avg_centers):
print("Starting calculations for", instrument, ":",
center_date)
if center_date > ref_hist_pd.date_obs.max(
) or center_date < ref_hist_pd.date_obs.min():
print("Date is out of instrument range, skipping.")
continue
# determine time range based off moving average centers
min_date = center_date - moving_width / 2
max_date = center_date + moving_width / 2
# get the correct date range to use for image combos
ref_pd_use = ref_hist_pd[
(ref_hist_pd['date_obs'] >= str(min_date))
& (ref_hist_pd['date_obs'] <= str(max_date))]
# save alpha/x as [1, 0] for reference instrument
alpha = 1
x = 0
db_funcs.store_iit_values(db_session, ref_pd_use, meth_name,
meth_desc, [alpha, x], create)
else:
# query euv_images for correct carrington rotation
query_instrument = [
instrument,
]
rot_images = db_funcs.query_euv_images_rot(
db_session,
rot_min=rot_min,
rot_max=rot_max,
instrument=query_instrument,
wavelength=wavelengths)
if rot_images.shape[0] == 0:
print("No images in timeframe for ", instrument, ". Skipping")
continue
# get time minimum and maximum for instrument
inst_time_min = rot_images.date_obs.min()
inst_time_max = rot_images.date_obs.max()
# if Stereo A or B has images before AIA, calc IIT for those weeks
if inst_time_min > calc_query_time_min:
all_images = db_funcs.query_euv_images(
db_session,
time_min=calc_query_time_min,
time_max=calc_query_time_max,
instrument=query_instrument,
wavelength=wavelengths)
if all_images.date_obs.min() < inst_time_min:
inst_time_min = all_images.date_obs.min()
moving_avg_centers, moving_width = lbcc.moving_averages(
inst_time_min, inst_time_max, weekday, number_of_days)
# calculate image cadence centers
range_min_date = moving_avg_centers[0] - moving_width / 2
range_max_date = moving_avg_centers[-1] + moving_width / 2
image_centers = synch_utils.get_dates(
time_min=range_min_date.astype(datetime.datetime),
time_max=range_max_date.astype(datetime.datetime),
map_freq=image_freq)
inst_hist_pd = db_funcs.query_hist(
db_session=db_session,
meth_id=method_id[1],
n_intensity_bins=n_intensity_bins,
lat_band=lat_band,
time_min=inst_time_min -
datetime.timedelta(days=number_of_days),
time_max=inst_time_max +
datetime.timedelta(days=number_of_days),
instrument=query_instrument,
wavelength=wavelengths)
# keep only one observation-histogram per image_center window
keep_ind = lbcc.cadence_choose(inst_hist_pd.date_obs,
image_centers, image_del)
inst_hist_pd = inst_hist_pd.iloc[keep_ind]
# convert binary to histogram data
mu_bin_edges, intensity_bin_edges, inst_full_hist = psi_d_types.binary_to_hist(
hist_binary=inst_hist_pd,
n_mu_bins=None,
n_intensity_bins=n_intensity_bins)
# loops through moving average centers
for date_index, center_date in enumerate(moving_avg_centers):
print("Starting calculations for", instrument, ":",
center_date)
if center_date > inst_hist_pd.date_obs.max(
) or center_date < inst_hist_pd.date_obs.min():
print("Date is out of instrument range, skipping.")
continue
# determine time range based off moving average centers
min_date = center_date - moving_width / 2
max_date = center_date + moving_width / 2
# get proper time-range of reference histograms
if center_date <= base_ref_center:
# if date is earlier than reference (AIA) first year, use reference (AIA) first year
ref_hist_use = ref_base_hist
else:
# get indices for calculation of reference histogram
ref_hist_ind = (ref_hist_pd['date_obs'] >= str(
min_date)) & (ref_hist_pd['date_obs'] <= str(max_date))
ref_hist_use = ref_full_hist[:, ref_hist_ind]
# get the correct date range to use for the instrument histogram
inst_hist_ind = (inst_hist_pd['date_obs'] >= str(min_date)) & (
inst_hist_pd['date_obs'] <= str(max_date))
inst_pd_use = inst_hist_pd[inst_hist_ind]
# get indices and histogram for calculation
inst_hist_use = inst_full_hist[:, inst_hist_ind]
# sum histograms
hist_fit = inst_hist_use.sum(axis=1)
hist_ref = ref_hist_use.sum(axis=1)
# normalize fit histogram
fit_sum = hist_fit.sum()
norm_hist_fit = hist_fit / fit_sum
# normalize reference histogram
ref_sum = hist_ref.sum()
norm_hist_ref = hist_ref / ref_sum
# get reference/fit peaks
ref_peak_index = np.argmax(
norm_hist_ref) # index of max value of hist_ref
ref_peak_val = norm_hist_ref[
ref_peak_index] # max value of hist_ref
fit_peak_index = np.argmax(
norm_hist_fit) # index of max value of hist_fit
fit_peak_val = norm_hist_fit[
fit_peak_index] # max value of hist_fit
# estimate correction coefficients that match fit_peak to ref_peak
alpha_est = fit_peak_val / ref_peak_val
x_est = intensity_bin_edges[
ref_peak_index] - alpha_est * intensity_bin_edges[
fit_peak_index]
init_pars = np.asarray([alpha_est, x_est], dtype=np.float64)
# calculate alpha and x
alpha_x_parameters = iit.optim_iit_linear(norm_hist_ref,
norm_hist_fit,
intensity_bin_edges,
init_pars=init_pars)
# save alpha and x to database
db_funcs.store_iit_values(db_session, inst_pd_use, meth_name,
meth_desc, alpha_x_parameters.x,
create)
end_time = time.time()
tot_time = end_time - start_time
time_tot = str(datetime.timedelta(minutes=tot_time))
print(
"Inter-instrument transformation fit parameters have been calculated and saved to the database."
)
print("Total elapsed time for IIT fit parameter calculation: " + time_tot)
return None
